Skip to content
naulon
Not checkedNot checked for notices yet.

Developing a system dynamics model for prediction of phosphorus in facultative stabilization ponds

Ebrahim Shahsavani, Ali Asghar Ebrahimi, Mohammad Hassan Ehrampoush and 3 more

AMB Express · 2019

doi:10.1186/s13568-019-0882-6 (opens in a new tab)

We email you when a new notice or a question about this paper appears.

Abstract

System dynamics is considered as a computer-aided approach to policy analysis and design. It includes the response and reaction of a system to external shocks. In the present research, following the sampling and testing phases, a system dynamics model was developed for modeling of phosphorus in facultative stabilization ponds. First, the scheme of soluble reactive phosphorous stock, its specifications and parameters were determined and created in the VenSim PLE 7.1 software. Then, mathematical relations were determined for each process. Finally, the model was calibrated and verified based on the data from the Yazd facultative ponds, Iran. Sensitivity analysis showed that the most important factors affecting phosphorus concentration in the ponds are the phosphorus settling rate, losses caused by algal respiration and excretion, while the losses caused by herbivorous zooplanktons, hydrolysis rate of inorganic carbon, and ratio of phosphorus to chlorophyll-a had the least importance. Results showed that, algal growth rate and phosphorus settling rate were important factors in phosphorus removal. Hence, with appropriate retention time in the pond, it can be managed more properly. The ratio of phosphorus to algae had less importance in the model. The ratio of carbon to phosphorus and rate of respiration of carnivorous zooplanktons did not affect the phosphorus concentration. It is recommended that this model can be used for pond management and overall assessment of facultative ponds.

Abstract licence: CC BY-NC-ND

References43

    43 more references, whose wording the paper’s licence keeps private.

    Reads

    Read by people on this page

    None yet.

    Checked or read by AI through naulon

    None yet.

    A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

    Are you an author?

    Is this your paper?

    Claim it to add your note and be paid when agents cite it. Free.